



import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		Optional[int]		,						lowerCAmelCase__:		List[str]  ):





			"""simple docstring"""


			if isinstance(lowerCAmelCase__		,						torch.Tensor  ):
						return image
			elif isinstance(lowerCAmelCase__		,						PIL.Image.Image  ):
						UpperCAmelCase_:		Optional[Any]							      =     [image]

			if isinstance(image[0]		,						PIL.Image.Image  ):
						UpperCAmelCase_:		Any							      =     [np.array(i.resize((w, h)		,						resample=PIL_INTERPOLATION["""lanczos"""]  )  )[None, :] for i in image]
						UpperCAmelCase_:		Union[str, Any]							      =     np.concatenate(lowerCAmelCase__		,						axis=0  )
						UpperCAmelCase_:		List[Any]							      =     np.array(lowerCAmelCase__  ).astype(np.floataa  ) / 255.0
						UpperCAmelCase_:		List[str]							      =     image.transpose(0		,						3		,						1		,						2  )
						UpperCAmelCase_:		Dict							      =     2.0 * image - 1.0
						UpperCAmelCase_:		Union[str, Any]							      =     torch.from_numpy(lowerCAmelCase__  )
			elif isinstance(image[0]		,						torch.Tensor  ):
						UpperCAmelCase_:		Union[str, Any]							      =     torch.cat(lowerCAmelCase__		,						dim=0  )
			return image







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[int]		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		Optional[int]		,						lowerCAmelCase__:		List[str]=0.9995  ):





			"""simple docstring"""


			if not isinstance(lowerCAmelCase__		,						np.ndarray  ):
						UpperCAmelCase_:		Dict							      =     True
						UpperCAmelCase_:		Optional[int]							      =     va.device
						UpperCAmelCase_:		int							      =     va.cpu().numpy()
						UpperCAmelCase_:		List[Any]							      =     va.cpu().numpy()

			UpperCAmelCase_:		Dict							      =     np.sum(va * va / (np.linalg.norm(lowerCAmelCase__  ) * np.linalg.norm(lowerCAmelCase__  ))  )
			if np.abs(lowerCAmelCase__  ) > DOT_THRESHOLD:
						UpperCAmelCase_:		int							      =     (1 - t) * va + t * va
			else:
						UpperCAmelCase_:		Any							      =     np.arccos(lowerCAmelCase__  )
						UpperCAmelCase_:		Any							      =     np.sin(lowerCAmelCase__  )
						UpperCAmelCase_:		str							      =     theta_a * t
						UpperCAmelCase_:		Tuple							      =     np.sin(lowerCAmelCase__  )
						UpperCAmelCase_:		int							      =     np.sin(theta_a - theta_t  ) / sin_theta_a
						UpperCAmelCase_:		Optional[int]							      =     sin_theta_t / sin_theta_a
						UpperCAmelCase_:		Optional[Any]							      =     sa * va + sa * va

			if inputs_are_torch:
						UpperCAmelCase_:		int							      =     torch.from_numpy(lowerCAmelCase__  ).to(lowerCAmelCase__  )

			return va







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		int							      =     F.normalize(lowerCAmelCase__		,						dim=-1  )
			UpperCAmelCase_:		Any							      =     F.normalize(lowerCAmelCase__		,						dim=-1  )
			return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )







def  lowerCAmelCase_	(lowerCAmelCase__:		str		,						lowerCAmelCase__:		str  ):





			"""simple docstring"""


			for param in model.parameters():
						UpperCAmelCase_:		str							      =     value







class 				_a				(   _lowerCAmelCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=None,   )       ->   Union[str, Any]:
						super().__init__()
						self.register_modules(
						    vae=SCREAMING_SNAKE_CASE_,   text_encoder=SCREAMING_SNAKE_CASE_,   clip_model=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   feature_extractor=SCREAMING_SNAKE_CASE_,   coca_model=SCREAMING_SNAKE_CASE_,   coca_tokenizer=SCREAMING_SNAKE_CASE_,   coca_transform=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Tuple							      =     (
						    feature_extractor.size
						    if isinstance(feature_extractor.size,   SCREAMING_SNAKE_CASE_					)
						    else feature_extractor.size["""shortest_edge"""]
						)
						UpperCAmelCase_:		List[str]							      =     transforms.Normalize(mean=feature_extractor.image_mean,   std=feature_extractor.image_std					)
						set_requires_grad(self.text_encoder,   SCREAMING_SNAKE_CASE_					)
						set_requires_grad(self.clip_model,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_ = "auto"					)       ->   Any:
						if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									UpperCAmelCase_:		Tuple							      =     self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						self.enable_attention_slicing(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[Any]:
						set_requires_grad(self.vae,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						set_requires_grad(self.vae,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						set_requires_grad(self.unet,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						set_requires_grad(self.unet,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Tuple:
						# get the original timestep using init_timestep
						UpperCAmelCase_:		Optional[Any]							      =     min(int(num_inference_steps * strength					),   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     max(num_inference_steps - init_timestep,   0					)
						UpperCAmelCase_:		Optional[Any]							      =     self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=None					)       ->   str:
						if not isinstance(SCREAMING_SNAKE_CASE_,   torch.Tensor					):
									raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(SCREAMING_SNAKE_CASE_					)}'					)

						UpperCAmelCase_:		Union[str, Any]							      =     image.to(device=SCREAMING_SNAKE_CASE_,   dtype=SCREAMING_SNAKE_CASE_					)

						if isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Optional[int]							      =     [
									    self.vae.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(SCREAMING_SNAKE_CASE_					)
									]
									UpperCAmelCase_:		int							      =     torch.cat(SCREAMING_SNAKE_CASE_,   dim=0					)
						else:
									UpperCAmelCase_:		Dict							      =     self.vae.encode(SCREAMING_SNAKE_CASE_					).latent_dist.sample(SCREAMING_SNAKE_CASE_					)

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						UpperCAmelCase_:		Optional[Any]							      =     0.1_8_2_1_5 * init_latents
						UpperCAmelCase_:		Tuple							      =     init_latents.repeat_interleave(SCREAMING_SNAKE_CASE_,   dim=0					)

						UpperCAmelCase_:		str							      =     randn_tensor(init_latents.shape,   generator=SCREAMING_SNAKE_CASE_,   device=SCREAMING_SNAKE_CASE_,   dtype=SCREAMING_SNAKE_CASE_					)

						# get latents
						UpperCAmelCase_:		Tuple							      =     self.scheduler.add_noise(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     init_latents

						return latents

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		List[Any]							      =     self.coca_transform(SCREAMING_SNAKE_CASE_					).unsqueeze(0					)
						with torch.no_grad(), torch.cuda.amp.autocast():
									UpperCAmelCase_:		List[str]							      =     self.coca_model.generate(transformed_image.to(device=self.device,   dtype=self.coca_model.dtype					)					)
						UpperCAmelCase_:		List[Any]							      =     self.coca_tokenizer.decode(generated[0].cpu().numpy()					)
						return generated.split("""<end_of_text>"""					)[0].replace("""<start_of_text>""",   """"""					).rstrip(""" .,"""					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   str:
						UpperCAmelCase_:		Union[str, Any]							      =     self.feature_extractor.preprocess(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     torch.from_numpy(clip_image_input["""pixel_values"""][0]					).unsqueeze(0					).to(self.device					).half()
						UpperCAmelCase_:		List[Any]							      =     self.clip_model.get_image_features(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     image_embeddings_clip / image_embeddings_clip.norm(p=2,   dim=-1,   keepdim=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     image_embeddings_clip.repeat_interleave(SCREAMING_SNAKE_CASE_,   dim=0					)
						return image_embeddings_clip

			@torch.enable_grad()
			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   List[str]:
						UpperCAmelCase_:		Tuple							      =     latents.detach().requires_grad_()

						UpperCAmelCase_:		Any							      =     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						# predict the noise residual
						UpperCAmelCase_:		Union[str, Any]							      =     self.unet(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_					).sample

						if isinstance(self.scheduler,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)					):
									UpperCAmelCase_:		Union[str, Any]							      =     self.scheduler.alphas_cumprod[timestep]
									UpperCAmelCase_:		List[Any]							      =     1 - alpha_prod_t
									# compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									UpperCAmelCase_:		List[Any]							      =     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

									UpperCAmelCase_:		Optional[int]							      =     torch.sqrt(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		List[str]							      =     pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler,   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Any							      =     self.scheduler.sigmas[index]
									UpperCAmelCase_:		Tuple							      =     latents - sigma * noise_pred
						else:
									raise ValueError(f'scheduler type {type(self.scheduler					)} not supported'					)

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						UpperCAmelCase_:		Optional[int]							      =     1 / 0.1_8_2_1_5 * sample
						UpperCAmelCase_:		Optional[int]							      =     self.vae.decode(SCREAMING_SNAKE_CASE_					).sample
						UpperCAmelCase_:		int							      =     (image / 2 + 0.5).clamp(0,   1					)

						UpperCAmelCase_:		Union[str, Any]							      =     transforms.Resize(self.feature_extractor_size					)(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     self.normalize(SCREAMING_SNAKE_CASE_					).to(latents.dtype					)

						UpperCAmelCase_:		Tuple							      =     self.clip_model.get_image_features(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     image_embeddings_clip / image_embeddings_clip.norm(p=2,   dim=-1,   keepdim=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[int]							      =     spherical_dist_loss(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					).mean() * clip_guidance_scale

						UpperCAmelCase_:		Optional[Any]							      =     -torch.autograd.grad(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)[0]

						if isinstance(self.scheduler,   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Tuple							      =     latents.detach() + grads * (sigma**2)
									UpperCAmelCase_:		Optional[int]							      =     noise_pred_original
						else:
									UpperCAmelCase_:		Union[str, Any]							      =     noise_pred_original - torch.sqrt(SCREAMING_SNAKE_CASE_					) * grads
						return noise_pred, latents

			@torch.no_grad()
			def __call__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 512,   SCREAMING_SNAKE_CASE_ = 512,   SCREAMING_SNAKE_CASE_ = 0.6,   SCREAMING_SNAKE_CASE_ = 50,   SCREAMING_SNAKE_CASE_ = 7.5,   SCREAMING_SNAKE_CASE_ = 1,   SCREAMING_SNAKE_CASE_ = 0.0,   SCREAMING_SNAKE_CASE_ = 100,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = "pil",   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = 0.8,   SCREAMING_SNAKE_CASE_ = 0.1,   SCREAMING_SNAKE_CASE_ = 0.1,   )       ->   List[Any]:
						if isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) and len(SCREAMING_SNAKE_CASE_					) != batch_size:
									raise ValueError(f'You have passed {batch_size} batch_size, but only {len(SCREAMING_SNAKE_CASE_					)} generators.'					)

						if height % 8 != 0 or width % 8 != 0:
									raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'					)

						if isinstance(SCREAMING_SNAKE_CASE_,   torch.Generator					) and batch_size > 1:
									UpperCAmelCase_:		Optional[Any]							      =     [generator] + [None] * (batch_size - 1)

						UpperCAmelCase_:		Dict							      =     [
						    ("""model""", self.coca_model is None),
						    ("""tokenizer""", self.coca_tokenizer is None),
						    ("""transform""", self.coca_transform is None),
						]
						UpperCAmelCase_:		str							      =     [x[0] for x in coca_is_none if x[1]]
						UpperCAmelCase_:		Optional[int]							      =     """, """.join(SCREAMING_SNAKE_CASE_					)
						# generate prompts with coca model if prompt is None
						if content_prompt is None:
									if len(SCREAMING_SNAKE_CASE_					):
												raise ValueError(
												    f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
												    f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'					)
									UpperCAmelCase_:		List[Any]							      =     self.get_image_description(SCREAMING_SNAKE_CASE_					)
						if style_prompt is None:
									if len(SCREAMING_SNAKE_CASE_					):
												raise ValueError(
												    f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
												    f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'					)
									UpperCAmelCase_:		Optional[Any]							      =     self.get_image_description(SCREAMING_SNAKE_CASE_					)

						# get prompt text embeddings for content and style
						UpperCAmelCase_:		Tuple							      =     self.tokenizer(
						    SCREAMING_SNAKE_CASE_,   padding="""max_length""",   max_length=self.tokenizer.model_max_length,   truncation=SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   )
						UpperCAmelCase_:		Optional[Any]							      =     self.text_encoder(content_text_input.input_ids.to(self.device					)					)[0]

						UpperCAmelCase_:		str							      =     self.tokenizer(
						    SCREAMING_SNAKE_CASE_,   padding="""max_length""",   max_length=self.tokenizer.model_max_length,   truncation=SCREAMING_SNAKE_CASE_,   return_tensors="""pt""",   )
						UpperCAmelCase_:		Any							      =     self.text_encoder(style_text_input.input_ids.to(self.device					)					)[0]

						UpperCAmelCase_:		Union[str, Any]							      =     slerp(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						# duplicate text embeddings for each generation per prompt
						UpperCAmelCase_:		Optional[Any]							      =     text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_,   dim=0					)

						# set timesteps
						UpperCAmelCase_:		Dict							      =     """offset""" in set(inspect.signature(self.scheduler.set_timesteps					).parameters.keys()					)
						UpperCAmelCase_:		List[Any]							      =     {}
						if accepts_offset:
									UpperCAmelCase_:		List[Any]							      =     1

						self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						self.scheduler.timesteps.to(self.device					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.get_timesteps(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   self.device					)
						UpperCAmelCase_:		int							      =     timesteps[:1].repeat(SCREAMING_SNAKE_CASE_					)

						# Preprocess image
						UpperCAmelCase_:		Any							      =     preprocess(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     self.prepare_latents(
						    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   text_embeddings.dtype,   self.device,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[int]							      =     preprocess(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     self.prepare_latents(
						    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   text_embeddings.dtype,   self.device,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[int]							      =     slerp(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						if clip_guidance_scale > 0:
									UpperCAmelCase_:		Tuple							      =     self.get_clip_image_embeddings(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		List[str]							      =     self.get_clip_image_embeddings(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		List[Any]							      =     slerp(
									    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						UpperCAmelCase_:		Optional[int]							      =     guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
									UpperCAmelCase_:		int							      =     content_text_input.input_ids.shape[-1]
									UpperCAmelCase_:		Tuple							      =     self.tokenizer([""""""],   padding="""max_length""",   max_length=SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					)
									UpperCAmelCase_:		List[Any]							      =     self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
									# duplicate unconditional embeddings for each generation per prompt
									UpperCAmelCase_:		str							      =     uncond_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_,   dim=0					)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									UpperCAmelCase_:		List[Any]							      =     torch.cat([uncond_embeddings, text_embeddings]					)

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						UpperCAmelCase_:		Dict							      =     (batch_size, self.unet.config.in_channels, height // 8, width // 8)
						UpperCAmelCase_:		List[str]							      =     text_embeddings.dtype
						if latents is None:
									if self.device.type == "mps":
												# randn does not work reproducibly on mps
												UpperCAmelCase_:		List[Any]							      =     torch.randn(SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_,   device="""cpu""",   dtype=SCREAMING_SNAKE_CASE_					).to(
												    self.device					)
									else:
												UpperCAmelCase_:		Tuple							      =     torch.randn(SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_,   device=self.device,   dtype=SCREAMING_SNAKE_CASE_					)
						else:
									if latents.shape != latents_shape:
												raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'					)
									UpperCAmelCase_:		Optional[int]							      =     latents.to(self.device					)

						# scale the initial noise by the standard deviation required by the scheduler
						UpperCAmelCase_:		Tuple							      =     latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						UpperCAmelCase_:		Dict							      =     """eta""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
						UpperCAmelCase_:		Optional[Any]							      =     {}
						if accepts_eta:
									UpperCAmelCase_:		Any							      =     eta

						# check if the scheduler accepts generator
						UpperCAmelCase_:		List[Any]							      =     """generator""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
						if accepts_generator:
									UpperCAmelCase_:		Optional[Any]							      =     generator

						with self.progress_bar(total=SCREAMING_SNAKE_CASE_					):
									for i, t in enumerate(SCREAMING_SNAKE_CASE_					):
												# expand the latents if we are doing classifier free guidance
												UpperCAmelCase_:		Optional[int]							      =     torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
												UpperCAmelCase_:		str							      =     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

												# predict the noise residual
												UpperCAmelCase_:		str							      =     self.unet(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_					).sample

												# perform classifier free guidance
												if do_classifier_free_guidance:
															UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     noise_pred.chunk(2					)
															UpperCAmelCase_:		Tuple							      =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# perform clip guidance
												if clip_guidance_scale > 0:
															UpperCAmelCase_:		str							      =     (
															    text_embeddings.chunk(2					)[1] if do_classifier_free_guidance else text_embeddings
															)
															UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     self.cond_fn(
															    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_:		Union[str, Any]							      =     self.scheduler.step(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						UpperCAmelCase_:		str							      =     1 / 0.1_8_2_1_5 * latents
						UpperCAmelCase_:		str							      =     self.vae.decode(SCREAMING_SNAKE_CASE_					).sample

						UpperCAmelCase_:		str							      =     (image / 2 + 0.5).clamp(0,   1					)
						UpperCAmelCase_:		Dict							      =     image.cpu().permute(0,   2,   3,   1					).numpy()

						if output_type == "pil":
									UpperCAmelCase_:		Union[str, Any]							      =     self.numpy_to_pil(SCREAMING_SNAKE_CASE_					)

						if not return_dict:
									return (image, None)

						return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_,   nsfw_content_detected=SCREAMING_SNAKE_CASE_					)











#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






